Application of ensemble learning in predicting shallow foundation settlement in cohesionless soil

Abstract

ABSTRACT Due to significant uncertainties associated with soil, it is challenging to anticipate settlement accurately for shallow footings on cohesionless soil. To produce more precise predictive settlement models, four ensemble learning models have been created in this study: Bagging, Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). The models are created utilizing a sizable database based on standard penetration tests (SPT). A variety of evaluation criteria, including R 2, RMSE, and MAE, were employed to rate the performance of the models. The analysis results showed that Bagging and XGBoost models demonstrate excellent performance with R 2 values of 0.901 and 0.915, respectively, surpassing other models studied here as well as other models from the literature. Consequently, Bagging and XGBoost can be effective methods for predicting settlement in shallow foundations on cohesionless soil.